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Dask VS PyXLL

Compare Dask VS PyXLL and see what are their differences

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Contents:
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  3. » Reviews
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Dask logo Dask

Dask natively scales Python Dask provides advanced parallelism for analytics, enabling performance at scale for the tools you love

PyXLL logo PyXLL

PyXLL is an Excel Add-In that enables developers to extend Excel’s capabilities with Python code.
  • Dask Landing page
    Landing page //
    2022-08-26
  • PyXLL Landing page
    Landing page //
    2023-06-14

Dask features and specs

  • Parallel Computing
    Dask allows you to write parallel, distributed computing applications with task scheduling, enabling efficient use of computational resources for processing large datasets.
  • Scale
    It scales from a single machine to a large cluster, providing flexibility to develop code locally on a laptop and then deploy to cloud or other high-performance environments.
  • Integration with Existing Ecosystem
    Dask integrates well with popular Python libraries like NumPy, pandas, and Scikit-learn, allowing users to leverage existing code and skills while scaling to larger datasets.
  • Flexibility
    Dask can handle both data parallel and task parallel workloads, giving developers the freedom to implement various algorithms and solutions efficiently.
  • Dynamic Task Scheduling
    Dask's dynamic task scheduler optimizes the execution of tasks based on available resources, reducing malfunction risks and improving resource utilization.

Possible disadvantages of Dask

  • Complexity in Setup
    Setting up Dask, particularly in distributed settings, can be complex and may require significant infrastructure management efforts.
  • Performance Overhead
    While Dask provides high-level abstractions for parallel computing, there can be performance overhead due to its abstractions and scheduling mechanics which might not match the performance of highly optimized, low-level code.
  • Limited Support for Some Libraries
    Dask's smart parallelization might not perfectly support all features of libraries like pandas or NumPy, potentially requiring workarounds.
  • Learning Curve
    Despite its integration with Python's data science stack, Dask presents a learning curve for those unfamiliar with parallel computing concepts.
  • Debugging Challenges
    Debugging parallel computations can be more challenging compared to single-threaded applications, and users need to understand the distributed computation model.

PyXLL features and specs

No features have been listed yet.

Dask videos

+ Add

DASK and Apache SparkGurpreet Singh Microsoft Corporation

More videos:

  • Review - VLOGTOBER : dask kitchen review ,groceries ,drinks
  • Review - Dask Futures: Introduction

PyXLL videos

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PyXLL Introduction

Category Popularity

0-100% (relative to Dask and PyXLL)

Dask

PyXLL

Workflows
100 100%
Workflows
0% 0
Project Management
0 0%
Project Management
100% 100
Databases
100 100%
Databases
0% 0
No Code
0 0%
No Code
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Dask and PyXLL

Dask Reviews

Python & ETL 2020: A List and Comparison of the Top Python ETL Tools
Dask: You can use Dask for Parallel computing via task scheduling. It can also process continuous data streams. Again, this is part of the "Blaze Ecosystem."
Source: www.xplenty.com

PyXLL Reviews

We have no reviews of PyXLL yet.
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Social recommendations and mentions

Based on our record, Dask seems to be more popular. It has been mentiond 16 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Dask mentions (16)

  • Large Scale Hydrology: Geocomputational tools that you use
    We're using a lot of Python. In addition to these, gridMET, Dask, HoloViz, and kerchunk. Source: over 3 years ago
  • msgspec - a fast & friendly JSON/MessagePack library
    I wrote this for speeding up the RPC messaging in dask, but figured it might be useful for others as well. The source is available on github here: https://github.com/jcrist/msgspec. Source: over 3 years ago
  • What does it mean to scale your python powered pipeline?
    Dask: Distributed data frames, machine learning and more. - Source: dev.to / almost 4 years ago
  • Data pipelines with Luigi
    To do that, we are efficiently using Dask, simply creating on-demand local (or remote) clusters on task run() method:. - Source: dev.to / almost 4 years ago
  • How to load 85.6 GB of XML data into a dataframe
    I’m quite sure dask helps and has a pandas like api though will use disk and not just RAM. Source: almost 4 years ago
View more

PyXLL mentions (0)

We have not tracked any mentions of PyXLL yet. Tracking of PyXLL recommendations started around Mar 2021.

What are some alternatives?

When comparing Dask and PyXLL, you can also consider the following products

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

xlwings - xlwings is a Python library that makes it easy to call Python from Excel and vice versa

NumPy - NumPy is the fundamental package for scientific computing with Python

Glide - Send lightning fast video messages, see responses live or whenever it's convenient. Get closer to the ones you love with video communication.

PySpark - PySpark Tutorial - Apache Spark is written in Scala programming language. To support Python with Spark, Apache Spark community released a tool, PySpark. Using PySpark, you can wor

Smartsheet - Smartsheet is an intuitive online project management tool enabling teams to increase productivity using cloud, collaboration, & mobile technologies.

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